Wavelet-based semblance analysis to determine muscle synergy for different handstand postures of Chilean circus athletes

Calderón-Díaz, Mailyn; Ulloa-Jiménez, Ricardo; Saavedra, Carolina; Salas, Rodrigo

doi:10.1080/10255842.2020.1867113

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…However, FFT did not present abrupt changes efficiently because sinusoidal waves were not localized in space or time. Unlike a sinusoidal wave, a wavelet could be scaled in space and shifted in time [48]. The continuous wavelet transform (CWT) decomposed the pressure signal into constituents of various frequencies while maintaining the temporal variation, i.e., frequency-time domain.…”

Section: Discussionmentioning

confidence: 99%

Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx

Wang

April

et al. 2023

Theor. Comput. Fluid Dyn.

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Snoring and obstructive sleep apnea (OSA) are often associated with uvula vibrations and pharynx constrictions. However, successful treatment of snoring or accurate diagnosis of OSA has been proven challenging. This study aimed to identify acoustic indexes that were sensitive to underlying airway structural or kinematic variations. Six physiologically realistic models were developed that consisted of three pharynx constriction levels (M1-3) and two uvula-flapping kinematics (K1-2). Direct numerical simulations (DNS) were performed to resolve spatial and temporal flow dynamics and an immersed boundary method was used to approximate the uvula vibrations. Time-varying acoustic pressures at six points in the pharynx were analyzed using different algorithms in frequency-or frequency-time domains. Signature flow structures formed near the uvula for different uvula motions and in the pharynx for different pharyngeal constriction levels. The fast Fourier transform (FFT) showed that the acoustic energy was mainly distributed in four peaks (flapping frequency and three harmonics) with descending magnitudes. Their amplitudes and distribution patterns differed among the six models but were not substantial. The continuous wavelet transforms (CWT) showed clearly separated acoustic cycles (in both frequency and time) in the uvula-induced flows and revealed a cascading bifurcation pattern in the inputoutput semblance map. Specifically, the multifractal spectrum was sensitive to uvula flapping kinematics but not pharynx constrictions. By contrast, the input-output cross-correlation and Hilbert phase space showed high sensitivity to pharynx constrictions but low sensitivity to uvula kinematics. The frequency-time analyses of DNS-predicted pressures offered insight into the acoustics signals that were not apparent in original signals and could be used individually or in combination in diagnosis or treatment planning for snoring/OSA patients.

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Section: Discussionmentioning

confidence: 99%

Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx

Wang

April

et al. 2023

Theor. Comput. Fluid Dyn.

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“…In sports and intense competition, there is a substantial risk of sustaining injuries arising from the demanding physical and psychological pressures. These injuries impact the athletes and have ripple effects on coaches, sponsors, teams, and clubs, compounded by the substantial medical expenses involved [ 1 , 2 , 3 , 4 ]. In sports like soccer, lower extremity injuries claim a significant share, amounting to 92% of the total injury count [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis

Calderón-Díaz,

Silvestre Aguirre,

Vásconez

et al. 2023

Sensors

Self Cite

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There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.

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Intelligent control model of athletes’ training intensity based on big data analysis of physical fitness

Zhang

2021

J. Phys.: Conf. Ser.

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Based on the analysis of big data, this paper designs the intelligent control model of sports athletes’ training progress. By constructing the evaluation system of sports athletes’ training intensity, it improves the intelligent control algorithm of sports athletes’ training intensity, and ensures the operation effect of the intelligent control model of sports athletes’ training intensity. In order to verify the effect of the model, simulation experiments are carried out, and the results show that most of the models are effective. From the perspective of analysis, the intelligent model of athletes’ training progress has outstanding practical value. The model can quickly and accurately formulate training plans, solve the data fluctuation phenomenon under the trend of big data division, and realize intelligent operation. Moreover, the fluency and accuracy are very high, which is worth promoting and applying.

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Wavelet-based semblance analysis to determine muscle synergy for different handstand postures of Chilean circus athletes

Cited by 5 publications

References 27 publications

Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx

Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx

Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis

Intelligent control model of athletes’ training intensity based on big data analysis of physical fitness

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